Reports to: Head of Analytics, Matrixed to Chief Product Officer

Why this role exists
Cvent is scaling its product analytics capability to serve a large, multi‑product portfolio (Attendee Hub, Registration/Event Management, OnArrival, Marketplace/CSN, Exhibitor Solutions, and Cvent Essentials). We need a senior leader to build the operating system for product analytics—from metric contracts and instrumentation to a governed semantic layer and self‑serve insights—so teams can move from question → decision in minutes, not weeks.

What you will own

• Metric Contracts & Semantic Layer: Define and govern product KPIs and their lineage (adoption, activation, engagement, feature usage, time‑to‑value, Events Under Management (EUM), retention) and tie them directly to commercial outcomes (GRR/NRR, expansion, contraction).
• Instrumentation Engineering: Standards, naming/versioning, tracking plans, CI checks, coverage dashboards, and error budgets for data quality (freshness, accuracy, completeness).
• Self‑Serve Insights & Enablement: A scalable, governed self‑serve model (standard dashboards + explores), data literacy curriculum, office hours, and durable documentation.
• Identity & Data Design: User/account identity resolution across web, mobile, onsite devices (e.g., badge printers/kiosks), and partner integrations; deterministic keys and join strategies.
• Analytics Operating Cadence: Monthly decision readouts, portfolio‑level rollups, and “What We Learned” syntheses that change roadmaps and bet sizing.
• Tooling Strategy & TCO: Rationalize and integrate the analytics stack (product analytics, BI/semantic layer, observability, feature flags); drive buy‑vs‑build decisions and vendor governance.
• Team & Org Design: Work closely with leaders / managers who can run Platform & Instrumentation, Decision Science, and Insights & Enablement. Establish clear interfaces with Data Engineering, Security/Privacy, PMM, CS, and UXR.

Note on experiments: While experimentation isn’t the primary focus today, you will establish right‑sized guardrails and a playbook (e.g., A/B where feasible, holdouts, basic power/MDE guidance, SRM detection) so the org is future‑ready without over‑rotating now.

• Publish the Cvent Product Metrics Charter (north stars, driver trees, metric definitions, ownership, SLA for freshness) and keep it current.
• Stand up tracking plans and CI checks tied to PRDs; reach high instrumentation coverage for critical flows across products.
• Build a governed semantic layer and standard portfolio dashboards that roll up by product, persona, and account.
• Launch a data literacy program (workshops, office hours, docs) to drive confident self‑serve use by PMs, PMM, UX, CS, and leaders.
• Partner with Data Engineering on data contracts, dbt models, observability, cost management, and access controls; partner with Security/Legal on PII, retention, and privacy‑by‑design.
• Operationalize account‑level analytics (seats/licenses, feature entitlements, health scoring, expansion/contraction funnels) with explicit links to GRR/NRR.
• Produce decision‑quality narratives (not just dashboards): monthly “What we learned,” portfolio scorecards, and ad‑hoc deep dives for exec forums.
• Hire, coach, and retain a high‑performing team; set career paths, operating rhythms, and quality bars.

Must‑have

• 10–12+ years in product analytics/decision science for enterprise or B2B SaaS; 4+ years leading managers and building multi‑disciplinary teams.
• Proven ownership of metric governance & semantic layers (e.g., LookML/semantic models or equivalent) across multiple products.
• Expert SQL; proficiency with Python for analysis and production‑grade notebooks.
• Demonstrated success establishing instrumentation standards, CI checks, and data quality SLAs (freshness/accuracy/completeness) in partnership with Data Engineering.
• Experience unifying user/account identity across surfaces and offline/onsite data sources.
• Track record driving self‑serve adoption and data literacy at scale (training, playbooks, enablement).
• Experience measuring and operationalizing GenAI/ML systems in production, including defining success metrics, evaluating offline and online performance, supporting experimentation and human-in-the-loop feedback, and translating model behavior into product and business decisions.
• Executive presence and storytelling: turning evidence into clear choices that change roadmaps and investment.

Nice‑to‑have

• Exposure to experimentation at scale (A/B, holdouts, basic variance reduction) and the judgment to right‑size usage.
• Experience mapping product behaviors to commercial metrics (GRR/NRR, expansion/contraction) and account health scoring.
• Familiarity with event‑driven architectures, product telemetry on mobile/edge devices, and privacy‑by‑design.

How we’ll measure success

• Instrumentation Coverage: ≥95% of GA features ship with validated tracking plans; minimal schema breakages escaping to prod.
• Reliability SLAs: Data freshness within target windows for core dashboards; accuracy/completeness within agreed error budgets.
• Self‑Serve Adoption & Satisfaction: High monthly active use by PMs in governed explores/dashboards; PM CSAT ≥ target.
• Decision Latency: Significant reduction in time from question → decision in pilot business units.
• Business Linkage: Documented cases where analytics led to changes in roadmap/investment and moved EUM, adoption, or GRR/NRR.

Key focus areas

• Platform & Instrumentation: Tracking plans, CI, observability, coverage dashboards, data contracts.
• Decision Science: Deep dives, driver trees, account health models, right‑sized experimentation playbook.
• Insights & Enablement: Standard dashboards, governed explores, literacy curriculum, office hours, documentation.

How you’ll work with partners

• Product Management: Metric definitions, priorities, evidence‑backed decisions.
• Data Engineering: Pipelines, models, contracts, observability, cost; joint SLAs.
• Security/Legal/Privacy: PII handling, retention, consent, governance.
• UX Research: Pair on mixed‑methods insights; Product Analytics focuses on quant, UXR on qual craft and Research Ops.
• PMM/CS/RevOps: Win/loss themes, adoption/usage insights, account health signals that tie to commercial outcomes.

Preferred tools & practices

Product analytics & telemetry (e.g., Mixpanel, Rudderstack, custom event pipelines), BI/semantic layer (e.g., Sigma), data warehouse (e.g., Snowflake), notebooks, observability/quality , feature flags (e.g., LaunchDarkly), documentation hubs, and modern CI/CD.